CDC Daily by State
davegoblue
2022-09-07
Background
This file is designed to use CDC data to assess coronavirus disease burden by state, including creating and analyzing state-level clusters.
Through March 7, 2021, The COVID Tracking Project collected and integrated data on tests, cases, hospitalizations, deaths, and the like by state and date. The latest code for using this data is available in Coronavirus_Statistics_CTP_v004.Rmd.
The COVID Tracking Project suggest that US federal data sources are now sufficiently robust to be used for analyses that previously relied on COVID Tracking Project. This code is an attempt to update modules in Coronavirus_Statistics_CTP_v004.Rmd to leverage US federal data.
The code in this module builds on code available in _v004, with function and mapping files updated:
- Generic_Added_Utility_Functions_202105_v001.R - generic functions
that can be used in other areas
- Coronavirus_CDC_Daily_Functions_v001.R - functions specific to coronavirus daily data
Broadly, the CDC data analyzed by this module includes:
- CDC case and death data by state and date are available for download
on the CDC
website
- CDC hospital data are available for download on the healthdata.gov
website
- CDC vaccines data are also available for download on the CDC website:
Functions and Mapping Files
The tidyverse package is loaded and functions are sourced:
# The tidyverse functions are routinely used without package::function format
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ dplyr 1.0.9
## ✔ tidyr 1.2.0 ✔ stringr 1.4.0
## ✔ readr 2.1.2 ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(geofacet)
# Functions are available in source file
source("./Generic_Added_Utility_Functions_202105_v001.R")
source("./Coronavirus_CDC_Daily_Functions_v002.R")A series of mapping files are also available to allow for parameterized processing. Mappings include:
- urlMapper - mapping file for urlType and url location to download
data
- renMapper - mapping file for renaming of variables in the raw data
file
- selfListMapper - mapping file for transformations by variable
type
- fullListMapper - mapping file for transformations across variable
types
- lstComboMapper - mapping file for elements to be combined by data
type (most common is to combine NYC and NYS data to NY if the file
provides them separately)
- uqMapper - mapping file for fields that should combine to be unique
keys for processed files
- lstFilterMapper - mapping file for filtering to subset (most common
is to keep 50 states and DC)
- vecSelectMapper - mapping file for variables to keep
- checkControlGroupMapper - mapping file for group_by() of control
total checks
- checkControlAggMapper - mapping file for numeric variables for
control total checks
- checkSimilarityMapper - mapping file for similarity checks to
perform
- plotSimilarityMaooer - mapping file for fields where differences in universe should be plotted
- keyAggMapper - mapping file for the aggregate-level control total
checks to perform
- perCapMapper - named vector that drives conversion from original
field name to per capita field name
- hhsMapper - named vector that drivers numerical variables to keep (and renaming) from HHS capacity data file
These default parameters are maintained in a separate .R file and can be sourced:
source("./Coronavirus_CDC_Daily_Default_Mappings_v002.R")Example Code Processing
The function is run to download and process the latest CDC case, hospitalization, and death data:
readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_220907.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_220907.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_220907.csv"
)
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_220805")$dfRaw$cdcDaily,
"cdcHosp"=readFromRDS("cdc_daily_220805")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_220805")$dfRaw$vax
)
cdc_daily_220907 <- readRunCDCDaily(thruLabel="Sep 05, 2022",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)## Rows: 57480 Columns: 15
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): submission_date, state, created_at, consent_cases, consent_deaths
## dbl (10): tot_cases, conf_cases, prob_cases, new_case, pnew_case, tot_death,...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 33
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2022-07-31 new_deaths 116 23 93 1.33812950
## 2 2022-07-30 new_deaths 130 34 96 1.17073171
## 3 2022-07-23 new_deaths 158 109 49 0.36704120
## 4 2022-07-24 new_deaths 170 126 44 0.29729730
## 5 2022-08-01 new_deaths 433 347 86 0.22051282
## 6 2022-07-28 new_deaths 518 434 84 0.17647059
## 7 2022-07-16 new_deaths 151 127 24 0.17266187
## 8 2022-07-29 new_deaths 639 543 96 0.16243655
## 9 2022-07-25 new_deaths 306 265 41 0.14360771
## 10 2022-08-03 new_deaths 716 632 84 0.12462908
## 11 2022-08-02 new_deaths 715 632 83 0.12323682
## 12 2022-07-27 new_deaths 703 634 69 0.10321616
## 13 2022-07-10 new_deaths 114 103 11 0.10138249
## 14 2022-07-22 new_deaths 628 580 48 0.07947020
## 15 2022-06-18 new_deaths 105 97 8 0.07920792
## 16 2022-07-26 new_deaths 643 596 47 0.07586764
## 17 2022-07-04 new_deaths 138 128 10 0.07518797
## 18 2022-07-18 new_deaths 361 337 24 0.06876791
## 19 2022-07-21 new_deaths 500 471 29 0.05973223
## 20 2022-07-09 new_deaths 110 104 6 0.05607477
## 21 2022-07-30 new_cases 38338 32823 5515 0.15500063
## 22 2022-07-31 new_cases 39803 35276 4527 0.12059298
## 23 2022-08-01 new_cases 126242 133013 6771 0.05223429
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 NC tot_deaths 11352019 11325521 26498 0.002336938
## 2 KY tot_deaths 6954858 6939424 15434 0.002221633
## 3 NC new_deaths 26101 25692 409 0.015793640
## 4 KY new_deaths 16647 16438 209 0.012634124
## 5 FL new_deaths 78609 77823 786 0.010049095
## 6 AL new_deaths 20081 19974 107 0.005342654
## 7 SC new_deaths 18211 18192 19 0.001043870
## 8 SC new_cases 1626423 1605165 21258 0.013156380
## 9 KY new_cases 1489715 1479668 10047 0.006767062
## 10 NC new_cases 3026839 3022204 4635 0.001532474
##
##
##
## Raw file for cdcDaily:
## Rows: 57,480
## Columns: 15
## $ date <date> 2021-03-11, 2021-12-01, 2022-01-02, 2021-09-01, 2021-0…
## $ state <chr> "KS", "ND", "AS", "ND", "IN", "FL", "TN", "PR", "PW", "…
## $ tot_cases <dbl> 297229, 163565, 11, 118491, 668765, 3510205, 64885, 173…
## $ conf_cases <dbl> 241035, 135705, NA, 107475, NA, NA, 64371, 144788, NA, …
## $ prob_cases <dbl> 56194, 27860, NA, 11016, NA, NA, 514, 29179, NA, NA, NA…
## $ new_cases <dbl> 0, 589, 0, 536, 487, 9979, 1816, 667, 0, 317, 0, 28, 8,…
## $ pnew_case <dbl> 0, 220, 0, 66, 0, 2709, 30, 274, 0, 0, 0, 5, 0, 46, 70,…
## $ tot_deaths <dbl> 4851, 1907, 0, 1562, 12710, 56036, 749, 2911, 0, 561, 0…
## $ conf_death <dbl> NA, NA, NA, NA, 12315, NA, 722, 2482, NA, NA, NA, 1601,…
## $ prob_death <dbl> NA, NA, NA, NA, 395, NA, 27, 429, NA, NA, NA, 366, NA, …
## $ new_deaths <dbl> 0, 9, 0, 1, 7, 294, 8, 8, 0, 12, 0, 0, 0, 5, 0, 4, 0, 0…
## $ pnew_death <dbl> 0, 0, 0, 0, 2, 26, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ created_at <chr> "03/12/2021 03:20:13 PM", "12/02/2021 02:35:20 PM", "01…
## $ consent_cases <chr> "Agree", "Agree", NA, "Agree", "Not agree", "Not agree"…
## $ consent_deaths <chr> "N/A", "Not agree", NA, "Not agree", "Agree", "Not agre…## Rows: 49367 Columns: 135
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (132): critical_staffing_shortage_today_yes, critical_staffing_shortage...
## lgl (1): geocoded_state
## date (1): date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 33
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2020-07-25 hosp_ped 3964 4594 630 0.1472307
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 ND inp 122358 122070 288 0.002356522
## 2 NH hosp_ped 1127 1167 40 0.034873583
## 3 KS hosp_ped 4891 4725 166 0.034525790
## 4 ME hosp_ped 2387 2338 49 0.020740741
## 5 KY hosp_ped 20285 20665 380 0.018559219
## 6 WV hosp_ped 5686 5753 67 0.011714311
## 7 VA hosp_ped 18388 18192 196 0.010716238
## 8 TN hosp_ped 22215 22423 208 0.009319414
## 9 NM hosp_ped 8054 8114 60 0.007422068
## 10 SC hosp_ped 9035 9092 57 0.006288961
## 11 DE hosp_ped 5277 5310 33 0.006234061
## 12 NJ hosp_ped 19499 19618 119 0.006084311
## 13 UT hosp_ped 10271 10210 61 0.005956740
## 14 MS hosp_ped 11803 11854 51 0.004311620
## 15 AL hosp_ped 20947 21025 78 0.003716764
## 16 VT hosp_ped 540 542 2 0.003696858
## 17 WY hosp_ped 859 856 3 0.003498542
## 18 MA hosp_ped 12619 12657 38 0.003006805
## 19 NC hosp_ped 30541 30453 88 0.002885530
## 20 PR hosp_ped 23021 22959 62 0.002696825
## 21 IL hosp_ped 44084 44202 118 0.002673131
## 22 AK hosp_ped 2664 2657 7 0.002631084
## 23 MO hosp_ped 39841 39939 98 0.002456756
## 24 PA hosp_ped 55078 55211 133 0.002411845
## 25 AR hosp_ped 12767 12747 20 0.001567767
## 26 CO hosp_ped 22421 22387 34 0.001517586
## 27 OH hosp_ped 91382 91261 121 0.001324989
## 28 AZ hosp_ped 27563 27532 31 0.001125329
## 29 MD hosp_ped 17240 17221 19 0.001102696
## 30 ND hosp_adult 115920 115630 290 0.002504859
##
##
##
## Raw file for cdcHosp:
## Rows: 49,367
## Columns: 135
## $ state <chr> …
## $ date <date> …
## $ critical_staffing_shortage_today_yes <dbl> …
## $ critical_staffing_shortage_today_no <dbl> …
## $ critical_staffing_shortage_today_not_reported <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> …
## $ hospital_onset_covid <dbl> …
## $ hospital_onset_covid_coverage <dbl> …
## $ inpatient_beds <dbl> …
## $ inpatient_beds_coverage <dbl> …
## $ inpatient_beds_used <dbl> …
## $ inpatient_beds_used_coverage <dbl> …
## $ inp <dbl> …
## $ inpatient_beds_used_covid_coverage <dbl> …
## $ previous_day_admission_adult_covid_confirmed <dbl> …
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> …
## $ previous_day_admission_adult_covid_suspected <dbl> …
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_suspected <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> …
## $ staffed_adult_icu_bed_occupancy <dbl> …
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> …
## $ hosp_adult <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> …
## $ hosp_ped <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> …
## $ total_staffed_adult_icu_beds <dbl> …
## $ total_staffed_adult_icu_beds_coverage <dbl> …
## $ inpatient_beds_utilization <dbl> …
## $ inpatient_beds_utilization_coverage <dbl> …
## $ inpatient_beds_utilization_numerator <dbl> …
## $ inpatient_beds_utilization_denominator <dbl> …
## $ percent_of_inpatients_with_covid <dbl> …
## $ percent_of_inpatients_with_covid_coverage <dbl> …
## $ percent_of_inpatients_with_covid_numerator <dbl> …
## $ percent_of_inpatients_with_covid_denominator <dbl> …
## $ inpatient_bed_covid_utilization <dbl> …
## $ inpatient_bed_covid_utilization_coverage <dbl> …
## $ inpatient_bed_covid_utilization_numerator <dbl> …
## $ inpatient_bed_covid_utilization_denominator <dbl> …
## $ adult_icu_bed_covid_utilization <dbl> …
## $ adult_icu_bed_covid_utilization_coverage <dbl> …
## $ adult_icu_bed_covid_utilization_numerator <dbl> …
## $ adult_icu_bed_covid_utilization_denominator <dbl> …
## $ adult_icu_bed_utilization <dbl> …
## $ adult_icu_bed_utilization_coverage <dbl> …
## $ adult_icu_bed_utilization_numerator <dbl> …
## $ adult_icu_bed_utilization_denominator <dbl> …
## $ geocoded_state <lgl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> …
## $ deaths_covid <dbl> …
## $ deaths_covid_coverage <dbl> …
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses <dbl> …
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses <dbl> …
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses <dbl> …
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used <dbl> …
## $ previous_week_therapeutic_b_bamlanivimab_courses_used <dbl> …
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used <dbl> …
## $ icu_patients_confirmed_influenza <dbl> …
## $ icu_patients_confirmed_influenza_coverage <dbl> …
## $ previous_day_admission_influenza_confirmed <dbl> …
## $ previous_day_admission_influenza_confirmed_coverage <dbl> …
## $ previous_day_deaths_covid_and_influenza <dbl> …
## $ previous_day_deaths_covid_and_influenza_coverage <dbl> …
## $ previous_day_deaths_influenza <dbl> …
## $ previous_day_deaths_influenza_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_coverage <dbl> …
## $ all_pediatric_inpatient_bed_occupied <dbl> …
## $ all_pediatric_inpatient_bed_occupied_coverage <dbl> …
## $ all_pediatric_inpatient_beds <dbl> …
## $ all_pediatric_inpatient_beds_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_coverage <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_coverage <dbl> …
## $ staffed_pediatric_icu_bed_occupancy <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_coverage <dbl> …
## $ total_staffed_pediatric_icu_beds <dbl> …
## $ total_staffed_pediatric_icu_beds_coverage <dbl> …## Rows: 36184 Columns: 96
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Date, Location
## dbl (94): MMWR_week, Distributed, Distributed_Janssen, Distributed_Moderna, ...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference: Distributed_Novavax Administered_Novavax Series_Complete_Novavax
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 4
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 36,184
## Columns: 96
## $ date <date> 2022-08-31, 2022-08-31, 2022-0…
## $ MMWR_week <dbl> 35, 35, 35, 35, 35, 35, 35, 35,…
## $ state <chr> "PW", "SD", "MA", "HI", "RI", "…
## $ Distributed <dbl> 47090, 2141765, 18793570, 38391…
## $ Distributed_Janssen <dbl> 3800, 92800, 626200, 124700, 90…
## $ Distributed_Moderna <dbl> 30000, 847500, 7168380, 1461820…
## $ Distributed_Pfizer <dbl> 13290, 1199665, 10993590, 22498…
## $ Distributed_Novavax <dbl> 0, 1800, 5400, 2800, 3200, 200,…
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Dist_Per_100K <dbl> 218698, 242101, 272667, 271153,…
## $ Distributed_Per_100k_5Plus <dbl> 231139, 260083, 287577, 288518,…
## $ Distributed_Per_100k_12Plus <dbl> 252561, 290172, 312354, 316997,…
## $ Distributed_Per_100k_18Plus <dbl> 283966, 320836, 339252, 344011,…
## $ Distributed_Per_100k_65Plus <dbl> 2363960, 1410250, 1607210, 1430…
## $ vxa <dbl> 49416, 1511407, 15773792, 31561…
## $ Administered_5Plus <dbl> 49373, 1507671, 15687231, 31448…
## $ Administered_12Plus <dbl> 46683, 1451767, 15028043, 30184…
## $ Administered_18Plus <dbl> 43018, 1359766, 14038745, 28178…
## $ Administered_65Plus <dbl> 5346, 453691, 3769576, 842523, …
## $ Administered_Janssen <dbl> 2357, 42334, 407539, 71355, 664…
## $ Administered_Moderna <dbl> 37794, 586034, 6193704, 1157104…
## $ Administered_Pfizer <dbl> 9098, 882891, 9171774, 1927032,…
## $ Administered_Novavax <dbl> 0, 0, 295, 10, 219, 1, 45, 25, …
## $ Administered_Unk_Manuf <dbl> 167, 148, 480, 697, 2259, 9, 25…
## $ Admin_Per_100k <dbl> 229500, 170846, 228854, 222915,…
## $ Admin_Per_100k_5Plus <dbl> 242345, 183083, 240044, 236338,…
## $ Admin_Per_100k_12Plus <dbl> 250378, 196689, 249770, 249232,…
## $ Admin_Per_100k_18Plus <dbl> 259410, 203693, 253421, 252497,…
## $ Admin_Per_100k_65Plus <dbl> 268373, 298734, 322370, 313850,…
## $ Recip_Administered <dbl> 49797, 1533994, 15858274, 31873…
## $ Administered_Dose1_Recip <dbl> 20575, 700878, 6982383, 1266721…
## $ Administered_Dose1_Pop_Pct <dbl> 95.0, 79.2, 95.0, 89.5, 95.0, 8…
## $ Administered_Dose1_Recip_5Plus <dbl> 20547, 698199, 6928829, 1259039…
## $ Administered_Dose1_Recip_5PlusPop_Pct <dbl> 95.0, 84.8, 95.0, 94.6, 95.0, 9…
## $ Administered_Dose1_Recip_12Plus <dbl> 19119, 668658, 6593289, 1196906…
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 95.0, 90.6, 95.0, 95.0, 95.0, 9…
## $ Administered_Dose1_Recip_18Plus <dbl> 17584, 622099, 6127404, 1107067…
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 95.0, 93.2, 95.0, 95.0, 95.0, 9…
## $ Administered_Dose1_Recip_65Plus <dbl> 1876, 182192, 1462735, 275645, …
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 94.2, 95.0, 95.0, 95.0, 95.0, 8…
## $ vxc <dbl> 18338, 563276, 5570460, 1128707…
## $ vxcpoppct <dbl> 85.2, 63.7, 80.8, 79.7, 84.9, 8…
## $ Series_Complete_5Plus <dbl> 18330, 563050, 5551263, 1126786…
## $ Series_Complete_5PlusPop_Pct <dbl> 90.0, 68.4, 84.9, 84.7, 89.4, 9…
## $ Series_Complete_12Plus <dbl> 17241, 539812, 5278994, 1071938…
## $ Series_Complete_12PlusPop_Pct <dbl> 92.5, 73.1, 87.7, 88.5, 92.3, 9…
## $ vxcgte18 <dbl> 15791, 503622, 4896487, 990569,…
## $ vxcgte18pct <dbl> 95.0, 75.4, 88.4, 88.8, 93.0, 9…
## $ vxcgte65 <dbl> 1811, 151094, 1165453, 252765, …
## $ vxcgte65pct <dbl> 90.9, 95.0, 95.0, 94.2, 95.0, 8…
## $ Series_Complete_Janssen <dbl> 2361, 39918, 384642, 66056, 611…
## $ Series_Complete_Moderna <dbl> 12724, 204498, 1968460, 371324,…
## $ Series_Complete_Pfizer <dbl> 3164, 318781, 3216940, 691089, …
## $ Series_Complete_Novavax <dbl> 0, 2, 38, 1, 52, 1, 6, 6, 38, 2…
## $ Series_Complete_Unk_Manuf <dbl> 82, 70, 290, 215, 602, 3, 591, …
## $ Series_Complete_Janssen_5Plus <dbl> 2361, 39914, 384637, 66028, 611…
## $ Series_Complete_Moderna_5Plus <dbl> 12724, 204289, 1955713, 370535,…
## $ Series_Complete_Pfizer_5Plus <dbl> 3163, 318775, 3210586, 690007, …
## $ Series_Complete_Unk_Manuf_5Plus <dbl> 82, 70, 289, 215, 587, 3, 591, …
## $ Series_Complete_Janssen_12Plus <dbl> 2361, 39912, 384612, 66026, 611…
## $ Series_Complete_Moderna_12Plus <dbl> 12724, 204257, 1953836, 370413,…
## $ Series_Complete_Pfizer_12Plus <dbl> 2074, 295572, 2940222, 635307, …
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 82, 69, 286, 191, 572, 3, 588, …
## $ Series_Complete_Janssen_18Plus <dbl> 2361, 39882, 383306, 65839, 611…
## $ Series_Complete_Moderna_18Plus <dbl> 12723, 204149, 1948279, 369555,…
## $ Series_Complete_Pfizer_18Plus <dbl> 625, 259525, 2564603, 555015, 4…
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 82, 64, 262, 159, 543, 3, 574, …
## $ Series_Complete_Janssen_65Plus <dbl> 227, 5079, 74665, 11821, 6832, …
## $ Series_Complete_Moderna_65Plus <dbl> 1542, 74263, 531381, 111196, 86…
## $ Series_Complete_Pfizer_65Plus <dbl> 40, 71727, 559321, 129727, 1005…
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 2, 25, 80, 21, 162, 0, 263, 69,…
## $ Additional_Doses <dbl> 12048, 248903, 2987198, 646528,…
## $ Additional_Doses_Vax_Pct <dbl> 65.7, 44.2, 53.6, 57.3, 56.1, 5…
## $ Additional_Doses_5Plus <dbl> 12048, 248900, 2987162, 646515,…
## $ Additional_Doses_5Plus_Vax_Pct <dbl> 65.7, 44.2, 53.8, 57.4, 56.2, 5…
## $ Additional_Doses_12Plus <dbl> 11872, 246180, 2938665, 637193,…
## $ Additional_Doses_12Plus_Vax_Pct <dbl> 68.9, 45.6, 55.7, 59.4, 58.4, 5…
## $ Additional_Doses_18Plus <dbl> 11181, 236970, 2791202, 606621,…
## $ Additional_Doses_18Plus_Vax_Pct <dbl> 70.8, 47.1, 57.0, 61.2, 60.1, 5…
## $ Additional_Doses_50Plus <dbl> 4815, 163923, 1630617, 376685, …
## $ Additional_Doses_50Plus_Vax_Pct <dbl> 80.1, 58.2, 66.0, 75.0, 71.4, 7…
## $ Additional_Doses_65Plus <dbl> 1575, 98849, 840257, 208155, 15…
## $ Additional_Doses_65Plus_Vax_Pct <dbl> 87.0, 65.4, 72.1, 82.4, 79.0, 7…
## $ Additional_Doses_Moderna <dbl> 10870, 109000, 1349812, 272149,…
## $ Additional_Doses_Pfizer <dbl> 1176, 136782, 1609614, 367759, …
## $ Additional_Doses_Janssen <dbl> 2, 3093, 27721, 6500, 5296, 217…
## $ Additional_Doses_Unk_Manuf <dbl> 0, 26, 45, 118, 129, 0, 438, 78…
## $ Second_Booster <dbl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Second_Booster_50Plus <dbl> 1126, 53725, 590595, 170399, 10…
## $ Second_Booster_50Plus_Vax_Pct <dbl> 23.4, 32.8, 36.2, 45.2, 34.4, 1…
## $ Second_Booster_65Plus <dbl> 383, 39382, 379347, 111314, 667…
## $ Second_Booster_65Plus_Vax_Pct <dbl> 24.3, 39.8, 45.1, 53.5, 43.5, 2…
## $ Second_Booster_Janssen <dbl> 0, 27, 253, 120, 151, 1, 119, 2…
## $ Second_Booster_Moderna <dbl> 1148, 24919, 309097, 87335, 498…
## $ Second_Booster_Pfizer <dbl> 22, 30731, 314869, 91565, 57827…
## $ Second_Booster_Unk_Manuf <dbl> 0, 2, 10, 15, 53, 0, 80, 28, 21…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 3.53e+10 5.16e+8 93993694 1025546 56522
## 2 after 3.51e+10 5.14e+8 92929415 1019927 48858
## 3 pctchg 6.80e- 3 4.57e-3 0.0113 0.00548 0.136
##
##
## Processed for cdcDaily:
## Rows: 48,858
## Columns: 6
## $ date <date> 2021-03-11, 2021-12-01, 2021-09-01, 2021-03-08, 2021-09-17…
## $ state <chr> "KS", "ND", "ND", "IN", "FL", "TN", "IA", "SD", "HI", "MA",…
## $ tot_cases <dbl> 297229, 163565, 118491, 668765, 3510205, 64885, 20015, 1226…
## $ tot_deaths <dbl> 4851, 1907, 1562, 12710, 56036, 749, 561, 1967, 17, 17818, …
## $ new_cases <dbl> 0, 589, 536, 487, 9979, 1816, 317, 28, 8, 451, 1040, 133, 0…
## $ new_deaths <dbl> 0, 9, 1, 7, 294, 8, 12, 0, 0, 5, 4, 0, 0, 5, 1, 3, 0, 0, 22…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 5.11e+7 4.45e+7 1229807 49367
## 2 after 5.09e+7 4.43e+7 1205197 47181
## 3 pctchg 5.37e-3 5.13e-3 0.0200 0.0443
##
##
## Processed for cdcHosp:
## Rows: 47,181
## Columns: 5
## $ date <date> 2021-01-06, 2021-01-06, 2020-12-31, 2020-12-30, 2020-12-29…
## $ state <chr> "MA", "OR", "SD", "RI", "OR", "OH", "LA", "WV", "VT", "WY",…
## $ inp <dbl> 2232, 583, 282, 471, 626, 5534, 1461, 242, 0, 71, 1, 91, 49…
## $ hosp_adult <dbl> 2209, 568, 280, 469, 615, 5443, 1449, 241, 0, 70, 1, 90, 48…
## $ hosp_ped <dbl> 23, 15, 2, 2, 11, 91, 12, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, …
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgt…¹ vxcgte18 vxcgt…² n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 4.14e+11 1.70e+11 1511640. 4.31e+10 2.24e+6 1.57e+11 1.78e+6 3.62e+4
## 2 after 2.00e+11 8.22e+10 1265373. 2.09e+10 1.98e+6 7.61e+10 1.51e+6 2.86e+4
## 3 pctchg 5.18e- 1 5.16e- 1 0.163 5.16e- 1 1.14e-1 5.16e- 1 1.54e-1 2.09e-1
## # … with abbreviated variable names ¹​vxcgte65pct, ²​vxcgte18pct
##
##
## Processed for vax:
## Rows: 28,611
## Columns: 9
## $ date <date> 2022-08-31, 2022-08-31, 2022-08-31, 2022-08-31, 2022-08-3…
## $ state <chr> "SD", "MA", "HI", "RI", "MT", "WY", "LA", "KS", "IN", "MS"…
## $ vxa <dbl> 1511407, 15773792, 3156198, 2353711, 1675440, 778457, 6536…
## $ vxc <dbl> 563276, 5570460, 1128707, 899544, 618143, 300240, 2526855,…
## $ vxcpoppct <dbl> 63.7, 80.8, 79.7, 84.9, 57.8, 51.9, 54.4, 63.3, 56.8, 53.0…
## $ vxcgte65 <dbl> 151094, 1165453, 252765, 194268, 180238, 84688, 642150, 44…
## $ vxcgte65pct <dbl> 95.0, 95.0, 94.2, 95.0, 87.3, 85.4, 86.7, 94.6, 88.5, 85.2…
## $ vxcgte18 <dbl> 503622, 4896487, 990569, 794734, 562722, 275699, 2323648, …
## $ vxcgte18pct <dbl> 75.4, 88.4, 88.8, 93.0, 67.0, 62.0, 65.2, 74.2, 67.0, 63.3…
##
## Integrated per capita data file:
## Rows: 49,071
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0…
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"…
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, …
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA…
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in Proj4 definition
saveToRDS(cdc_daily_220907, ovrWriteError=FALSE)The function is run to download and process the latest hospitalization data:
# Run for latest data, save as RDS
indivHosp_20220907 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20220907.csv")##
## File ./RInputFiles/Coronavirus/HHS_Hospital_20220907.csv already exists
## File will not be downloaded since ovrWrite is not TRUE## Rows: 269456 Columns: 128
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (11): hospital_pk, state, ccn, hospital_name, address, city, zip, hosp...
## dbl (114): total_beds_7_day_avg, all_adult_hospital_beds_7_day_avg, all_adu...
## lgl (2): is_metro_micro, is_corrected
## date (1): collection_week
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.## Rows: 269,456
## Columns: 128
## $ hospital_pk <chr> …
## $ collection_week <date> …
## $ state <chr> …
## $ ccn <chr> …
## $ hospital_name <chr> …
## $ address <chr> …
## $ city <chr> …
## $ zip <chr> …
## $ hospital_subtype <chr> …
## $ fips_code <chr> …
## $ is_metro_micro <lgl> …
## $ total_beds_7_day_avg <dbl> …
## $ all_adult_hospital_beds_7_day_avg <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_avg <dbl> …
## $ inpatient_beds_used_7_day_avg <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg <dbl> …
## $ inpatient_beds_used_covid_7_day_avg <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg <dbl> …
## $ inpatient_beds_7_day_avg <dbl> …
## $ total_icu_beds_7_day_avg <dbl> …
## $ total_staffed_adult_icu_beds_7_day_avg <dbl> …
## $ icu_beds_used_7_day_avg <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_avg <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg <dbl> …
## $ icu_patients_confirmed_influenza_7_day_avg <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg <dbl> …
## $ total_beds_7_day_sum <dbl> …
## $ all_adult_hospital_beds_7_day_sum <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_sum <dbl> …
## $ inpatient_beds_used_7_day_sum <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum <dbl> …
## $ inpatient_beds_used_covid_7_day_sum <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum <dbl> …
## $ inpatient_beds_7_day_sum <dbl> …
## $ total_icu_beds_7_day_sum <dbl> …
## $ total_staffed_adult_icu_beds_7_day_sum <dbl> …
## $ icu_beds_used_7_day_sum <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_sum <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum <dbl> …
## $ icu_patients_confirmed_influenza_7_day_sum <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum <dbl> …
## $ total_beds_7_day_coverage <dbl> …
## $ all_adult_hospital_beds_7_day_coverage <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_coverage <dbl> …
## $ inpatient_beds_used_7_day_coverage <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage <dbl> …
## $ inpatient_beds_used_covid_7_day_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> …
## $ inpatient_beds_7_day_coverage <dbl> …
## $ total_icu_beds_7_day_coverage <dbl> …
## $ total_staffed_adult_icu_beds_7_day_coverage <dbl> …
## $ icu_beds_used_7_day_coverage <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage <dbl> …
## $ icu_patients_confirmed_influenza_7_day_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage <dbl> …
## $ previous_day_admission_adult_covid_confirmed_7_day_sum <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum` <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum <dbl> …
## $ previous_day_covid_ED_visits_7_day_sum <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_sum <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum` <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum <dbl> …
## $ previous_day_total_ED_visits_7_day_sum <dbl> …
## $ previous_day_admission_influenza_confirmed_7_day_sum <dbl> …
## $ geocoded_hospital_address <chr> …
## $ hhs_ids <chr> …
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage <dbl> …
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_none_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_one_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_all_7_day <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_one_7_day <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_all_7_day <dbl> …
## $ is_corrected <lgl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_avg <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_coverage <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_sum <dbl> …
## $ all_pediatric_inpatient_beds_7_day_avg <dbl> …
## $ all_pediatric_inpatient_beds_7_day_coverage <dbl> …
## $ all_pediatric_inpatient_beds_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_7_day_sum <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_avg <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_coverage <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_sum <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_avg <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_coverage <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_sum <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_avg <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_coverage <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_sum <dbl> …
##
## Hospital Subtype Counts:
## # A tibble: 4 × 2
## hospital_subtype n
## <chr> <int>
## 1 Childrens Hospitals 5077
## 2 Critical Access Hospitals 72331
## 3 Long Term 18519
## 4 Short Term 173529
##
## Records other than 50 states and DC
## # A tibble: 5 × 2
## state n
## <chr> <int>
## 1 AS 54
## 2 GU 106
## 3 MP 46
## 4 PR 2864
## 5 VI 106
##
## Record types for key metrics
## # A tibble: 10 × 5
## name `NA` Posit…¹ Value…² Total
## <chr> <int> <int> <int> <int>
## 1 all_adult_hospital_beds_7_day_avg 64869 204079 508 269456
## 2 all_adult_hospital_inpatient_bed_occupied_7_day… 143 247227 22086 269456
## 3 icu_beds_used_7_day_avg 64 237310 32082 269456
## 4 inpatient_beds_7_day_avg 67 268366 1023 269456
## 5 inpatient_beds_used_7_day_avg 51 248042 21363 269456
## 6 inpatient_beds_used_covid_7_day_avg 32 182000 87424 269456
## 7 staffed_icu_adult_patients_confirmed_and_suspec… 162 184312 84982 269456
## 8 total_adult_patients_hospitalized_confirmed_and… 121 181944 87391 269456
## 9 total_beds_7_day_avg 63149 206009 298 269456
## 10 total_icu_beds_7_day_avg 74 255402 13980 269456
## # … with abbreviated variable names ¹​Positive, ²​`Value -999999`## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
saveToRDS(indivHosp_20220907, ovrWriteError=FALSE)##
## File already exists: ./RInputFiles/Coronavirus/indivHosp_20220907.RDS
##
## Not replacing the existing file since ovrWrite=FALSE## NULLPost-processing is run, including hospital summaries:
# Create pivoted burden data
burdenPivotList_220907 <- postProcessCDCDaily(cdc_daily_220907,
dataThruLabel="Aug 2022",
keyDatesBurden=c("2022-08-31", "2022-02-28",
"2021-08-31", "2021-02-28"
),
keyDatesVaccine=c("2022-08-31", "2022-03-31",
"2021-10-31", "2021-05-31"
),
returnData=TRUE
)## Joining, by = "state"##
## *** File has been checked for uniqueness by: state date name## Warning: Removed 24 row(s) containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 9 row(s) containing missing values (geom_path).
# Create hospitalized per capita data
hospPerCap_220907 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"),
lst=burdenPivotList_220907,
popVar="pop2019",
excludeState=c(),
cumStartDate="2020-07-15"
)## Warning: Removed 18 row(s) containing missing values (geom_path).
burdenPivotList_220907$hospAge %>%
group_by(adultPed, confSusp, age, name) %>%
summarize(value=sum(value, na.rm=TRUE), n=n(), .groups="drop")## # A tibble: 18 × 6
## adultPed confSusp age name value n
## <chr> <chr> <chr> <chr> <dbl> <int>
## 1 adult confirmed 0-19 previous_day_admission_adult_covid_con… 4.70e4 49367
## 2 adult confirmed 20-29 previous_day_admission_adult_covid_con… 2.86e5 49367
## 3 adult confirmed 30-39 previous_day_admission_adult_covid_con… 4.12e5 49367
## 4 adult confirmed 40-49 previous_day_admission_adult_covid_con… 4.96e5 49367
## 5 adult confirmed 50-59 previous_day_admission_adult_covid_con… 7.91e5 49367
## 6 adult confirmed 60-69 previous_day_admission_adult_covid_con… 1.04e6 49367
## 7 adult confirmed 70-79 previous_day_admission_adult_covid_con… 1.04e6 49367
## 8 adult confirmed 80+ previous_day_admission_adult_covid_con… 9.32e5 49367
## 9 adult suspected 0-19 previous_day_admission_adult_covid_sus… 3.83e4 49367
## 10 adult suspected 20-29 previous_day_admission_adult_covid_sus… 2.56e5 49367
## 11 adult suspected 30-39 previous_day_admission_adult_covid_sus… 3.35e5 49367
## 12 adult suspected 40-49 previous_day_admission_adult_covid_sus… 3.39e5 49367
## 13 adult suspected 50-59 previous_day_admission_adult_covid_sus… 5.37e5 49367
## 14 adult suspected 60-69 previous_day_admission_adult_covid_sus… 7.38e5 49367
## 15 adult suspected 70-79 previous_day_admission_adult_covid_sus… 7.19e5 49367
## 16 adult suspected 80+ previous_day_admission_adult_covid_sus… 6.55e5 49367
## 17 ped confirmed 0-19 previous_day_admission_pediatric_covid… 1.67e5 49367
## 18 ped suspected 0-19 previous_day_admission_pediatric_covid… 3.74e5 49367saveToRDS(burdenPivotList_220907, ovrWriteError=FALSE)
saveToRDS(hospPerCap_220907, ovrWriteError=FALSE)Peaks and valleys of key metrics are also updated:
peakValleyCDCDaily(cdc_daily_220907)## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## # A tibble: 7,740 × 8
## date state vxa vxc vxa_isPeak vxc_isPeak vxa_isValley vxc_isValley
## <date> <chr> <dbl> <dbl> <lgl> <lgl> <lgl> <lgl>
## 1 2020-12-01 CA NA NA FALSE FALSE FALSE FALSE
## 2 2020-12-01 FL NA NA FALSE FALSE FALSE FALSE
## 3 2020-12-01 GA NA NA FALSE FALSE FALSE FALSE
## 4 2020-12-01 IL NA NA FALSE FALSE FALSE FALSE
## 5 2020-12-01 MI NA NA FALSE FALSE FALSE FALSE
## 6 2020-12-01 NC NA NA FALSE FALSE FALSE FALSE
## 7 2020-12-01 NJ NA NA FALSE FALSE FALSE FALSE
## 8 2020-12-01 NY NA NA FALSE FALSE FALSE FALSE
## 9 2020-12-01 OH NA NA FALSE FALSE FALSE FALSE
## 10 2020-12-01 PA NA NA FALSE FALSE FALSE FALSE
## # … with 7,730 more rows
## # ℹ Use `print(n = ...)` to see more rowsHospital capacity is updated using a mix of old data (for 2021) and new data:
identical(names(indivHosp_20220907), names(readFromRDS("indivHosp_20220704")))## [1] TRUEmodHospData <- bind_rows(filter(readFromRDS("indivHosp_20220704"), lubridate::year(collection_week)<2022),
filter(indivHosp_20220907, lubridate::year(collection_week)>=2022),
.id="src"
)
updated_modStateHosp_20220907 <- hospitalCapacityCDCDaily(modHospData,
plotSub="Aug 2020 to Aug 2022\nOld data used pre-2022"
)
Data availability by source and time is assessed:
# Temporary function to aggregate data
tempCounter <- function(df) {
df %>%
select(hospital_pk, collection_week, all_of(names(hhsMapper))) %>%
colRenamer(vecRename=hhsMapper) %>%
pivot_longer(-c(hospital_pk, collection_week)) %>%
filter(!is.na(value), value>0) %>%
count(collection_week, name)
}
dfTemp <- bind_rows(tempCounter(indivHosp_20220907), tempCounter(readFromRDS("indivHosp_20220704")), .id="src")
dfTemp %>%
select(collection_week, name) %>%
unique() %>%
bind_rows(., ., .id="src") %>%
full_join(dfTemp, by=c("src", "collection_week", "name")) %>%
mutate(src=c("1"="SEP-2022", "2"="JUL-2022")[src]) %>%
mutate(n=ifelse(is.na(n), 0, n)) %>%
ggplot(aes(x=collection_week, y=n)) +
geom_line(aes(group=src, color=src)) +
facet_wrap(~name) +
labs(title="Number of hospitals in US reporting >0 on metric by week", x=NULL, y="# Hospitals Reporting > 0") +
scale_color_discrete("Data Source:")
dfTemp %>%
select(collection_week, name) %>%
unique() %>%
bind_rows(., ., .id="src") %>%
full_join(dfTemp, by=c("src", "collection_week", "name")) %>%
mutate(src=c("1"="SEP-2022", "2"="JUL-2022")[src]) %>%
mutate(n=ifelse(is.na(n), 0, n)) %>%
group_by(collection_week, name) %>%
summarize(delta=sum(ifelse(src=="SEP-2022", n, 0)-ifelse(src!="SEP-2022", n, 0)), .groups="drop") %>%
ggplot(aes(x=collection_week, y=delta)) +
geom_line(aes(color=case_when(delta>=0 ~ "darkgreen", TRUE ~ "red"))) +
geom_hline(yintercept=0, lty=2) +
geom_vline(xintercept=c(as.Date("2021-08-20"), as.Date("2022-06-24")), lty=2) +
scale_color_identity(NULL) +
facet_wrap(~name) +
labs(title="Delta in Number of hospitals in US reporting >0 on metric by week",
subtitle="Trend break dashed lines at 2021-08-20 and 2022-06-24",
x=NULL,
y="Delta in # Hospitals Reporting > 0"
)
The process is converted to functional form:
multiSourceDataCombine <- function(lst, timeVec, keyVar="collection_week", idName="src") {
# FUNCTION ARGUMENTS:
# lst: list of data frames to be combined
# timeVec: vector of time cut points (data before timeVec[1] taken from lst[[1]], etc.)
# keyVar: variable describing time in the data
# idName: name of column for .id when files combined
# Check list lengths
if(length(lst)==0) {
cat("\nEmpty list passed, returning 0x0 tibble\n")
return(tibble::tibble())
} else if (length(lst)==1) {
cat("\nList of length 1 passed, returning item in list as-is")
return(lst[[1]])
}
# Check that timeVec matches
if(length(lst) != length(timeVec) + 1) stop("\nMismatch of lst and timeVec\n")
# Check that all data frames have the same column names in the same order
vecNames <- names(lst[[1]])
for(n in 2:length(lst)) if(!isTRUE(identical(names(lst[[n]]), vecNames))) stop("\nName mismatch in files\n")
# Combine data
bind_rows(lst, .id=idName) %>%
mutate(srcNum=as.integer(get(idName)),
dateMin=ifelse(srcNum==1, NA, timeVec[srcNum-1]),
dateMax=ifelse(srcNum==max(srcNum), NA, timeVec[srcNum])
) %>%
filter(is.na(dateMin) | get(keyVar) >= dateMin,
is.na(dateMax) | get(keyVar) < dateMax
) %>%
select(-srcNum, -dateMin, -dateMax)
}
# Create modified hospital data
multiSourceHosp_20220902 <- multiSourceDataCombine(list(readFromRDS("indivHosp_20220704"),
indivHosp_20220907
),
timeVec=as.Date("2022-01-01")
)
# Confirm that function produces expected output
multiSourceHosp_20220902 %>%
select(-src) %>%
identical(modHospData %>% select(-src))## [1] TRUEThe updated hospital data are then plotted:
# Run hospital plots
modStateHosp_20220902 <- hospitalCapacityCDCDaily(multiSourceHosp_20220902,
plotSub="Aug 2020 to Aug 2022\nOld data used pre-2022"
)
Data Refreshes
Data From 2022-10-02
The latest CDC case, hospitalization, and death data are downloaded and processed:
readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_221002.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_221002.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_221002.csv"
)
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_220907")$dfRaw$cdcDaily,
"cdcHosp"=readFromRDS("cdc_daily_220907")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_220907")$dfRaw$vax
)
cdc_daily_221002 <- readRunCDCDaily(thruLabel="Sep 30, 2022",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)## Rows: 58980 Columns: 15
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): submission_date, state, created_at, consent_cases, consent_deaths
## dbl (10): tot_cases, conf_cases, prob_cases, new_case, pnew_case, tot_death,...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 25
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2022-09-04 new_deaths 109 20 89 1.37984496
## 2 2022-09-03 new_deaths 116 24 92 1.31428571
## 3 2022-08-27 new_deaths 163 91 72 0.56692913
## 4 2022-09-05 new_deaths 106 60 46 0.55421687
## 5 2022-08-28 new_deaths 117 87 30 0.29411765
## 6 2022-08-20 new_deaths 139 111 28 0.22400000
## 7 2021-03-03 new_deaths 1899 1547 352 0.20429483
## 8 2022-08-06 new_deaths 174 142 32 0.20253165
## 9 2022-08-13 new_deaths 137 112 25 0.20080321
## 10 2020-09-14 new_deaths 376 453 77 0.18576598
## 11 2021-03-02 new_deaths 1134 1357 223 0.17904456
## 12 2022-08-21 new_deaths 122 102 20 0.17857143
## 13 2022-07-02 new_deaths 146 124 22 0.16296296
## 14 2022-09-01 new_deaths 499 428 71 0.15318231
## 15 2022-07-30 new_deaths 151 130 21 0.14946619
## 16 2022-07-04 new_deaths 120 138 18 0.13953488
## 17 2021-03-18 new_deaths 961 843 118 0.13082040
## 18 2022-06-27 new_deaths 259 295 36 0.12996390
## 19 2022-09-02 new_deaths 509 453 56 0.11642412
## 20 2022-08-14 new_deaths 115 103 12 0.11009174
## 21 2022-05-30 new_deaths 78 86 8 0.09756098
## 22 2022-04-25 new_deaths 181 199 18 0.09473684
## 23 2021-03-19 new_deaths 1080 1182 102 0.09018568
## 24 2022-07-09 new_deaths 120 110 10 0.08695652
## 25 2022-08-25 new_deaths 536 492 44 0.08560311
## 26 2022-08-30 new_deaths 633 582 51 0.08395062
## 27 2022-04-04 new_deaths 325 353 28 0.08259587
## 28 2022-08-26 new_deaths 674 621 53 0.08185328
## 29 2022-07-16 new_deaths 163 151 12 0.07643312
## 30 2022-08-01 new_deaths 402 433 31 0.07425150
## 31 2021-10-11 new_deaths 983 915 68 0.07165437
## 32 2022-06-20 new_deaths 140 150 10 0.06896552
## 33 2021-12-28 new_deaths 2334 2185 149 0.06594379
## 34 2022-08-31 new_deaths 778 830 52 0.06467662
## 35 2020-09-15 new_deaths 827 777 50 0.06234414
## 36 2020-09-21 new_deaths 547 580 33 0.05856256
## 37 2021-02-28 new_deaths 1027 1088 61 0.05768322
## 38 2022-08-19 new_deaths 622 588 34 0.05619835
## 39 2021-03-01 new_deaths 1333 1262 71 0.05472062
## 40 2021-06-28 new_deaths 183 193 10 0.05319149
## 41 2021-10-04 new_deaths 1298 1234 64 0.05055292
## 42 2022-09-03 new_cases 26751 14942 11809 0.56647399
## 43 2022-09-04 new_cases 26642 18143 8499 0.37954672
## 44 2022-08-27 new_cases 31520 27723 3797 0.12818392
## 45 2022-08-20 new_cases 31149 27757 3392 0.11516654
## 46 2022-07-04 new_cases 46367 50779 4412 0.09083236
## 47 2022-08-06 new_cases 36583 33586 2997 0.08542234
## 48 2022-08-28 new_cases 29299 26941 2358 0.08385491
## 49 2022-08-13 new_cases 33397 30736 2661 0.08298380
## 50 2022-07-30 new_cases 41625 38338 3287 0.08221302
## 51 2022-08-29 new_cases 80569 87383 6814 0.08114223
## 52 2022-08-14 new_cases 25021 23110 1911 0.07940828
## 53 2022-08-21 new_cases 30913 28587 2326 0.07818487
## 54 2022-07-11 new_cases 115394 124421 9027 0.07528303
## 55 2022-09-01 new_cases 106616 99807 6809 0.06597133
## 56 2021-10-04 new_cases 92025 86161 5864 0.06581886
## 57 2021-09-20 new_cases 98599 92534 6065 0.06346366
## 58 2022-07-09 new_cases 58075 54584 3491 0.06197463
## 59 2022-07-23 new_cases 56226 52972 3254 0.05959816
## 60 2022-09-02 new_cases 107474 101329 6145 0.05885931
## 61 2022-07-02 new_cases 51723 48818 2905 0.05778737
## 62 2022-08-07 new_cases 37208 35135 2073 0.05731031
## 63 2021-09-06 new_cases 115878 109526 6352 0.05636102
## 64 2022-07-18 new_cases 103308 109131 5823 0.05482044
## 65 2021-09-13 new_cases 119108 112822 6286 0.05420601
## 66 2022-07-31 new_cases 42016 39803 2213 0.05409501
## 67 2021-09-27 new_cases 107419 101840 5579 0.05332148
## 68 2022-07-16 new_cases 58177 55197 2980 0.05256937
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 VA tot_deaths 9582222 9605584 23362 0.002435088
## 2 KY tot_deaths 7519333 7506070 13263 0.001765410
## 3 VA tot_cases 726116885 728276276 2159391 0.002969474
## 4 KY new_deaths 16974 16757 217 0.012866503
## 5 AL new_deaths 20360 20203 157 0.007741045
## 6 VA new_deaths 21479 21334 145 0.006773644
## 7 FL new_deaths 80737 80209 528 0.006561207
## 8 KS new_deaths 9001 9054 53 0.005870950
## 9 NC new_deaths 26416 26338 78 0.002957122
## 10 OK new_deaths 13708 13746 38 0.002768267
## 11 SC new_deaths 18313 18263 50 0.002734033
## 12 SC new_cases 1688432 1674281 14151 0.008416419
## 13 KY new_cases 1559169 1546719 12450 0.008017031
## 14 NC new_cases 3143278 3123308 19970 0.006373486
## 15 CO new_cases 1637839 1634396 3443 0.002104372
## 16 WA new_cases 1789708 1787212 2496 0.001395614
##
##
##
## Raw file for cdcDaily:
## Rows: 58,980
## Columns: 15
## $ date <date> 2021-03-11, 2021-12-01, 2020-04-07, 2020-04-08, 2020-0…
## $ state <chr> "KS", "ND", "AS", "AR", "AR", "ND", "IN", "AR", "NY", "…
## $ tot_cases <dbl> 297229, 163565, 0, 1071, 0, 118491, 668765, 56199, 1882…
## $ conf_cases <dbl> 241035, 135705, NA, NA, NA, 107475, NA, NA, NA, 144788,…
## $ prob_cases <dbl> 56194, 27860, NA, NA, NA, 11016, NA, NA, NA, 29179, 125…
## $ new_cases <dbl> 0, 589, 0, 78, 0, 536, 487, 547, 318, 667, 154, 1509, 0…
## $ pnew_case <dbl> 0, 220, NA, NA, NA, 66, 0, 0, 0, 274, 43, 0, 0, 616, 0,…
## $ tot_deaths <dbl> 4851, 1907, 0, 18, 0, 1562, 12710, 674, 8822, 2911, 115…
## $ conf_death <dbl> NA, NA, NA, NA, NA, NA, 12315, NA, NA, 2482, 9205, NA, …
## $ prob_death <dbl> NA, NA, NA, NA, NA, NA, 395, NA, NA, 429, 2325, NA, 152…
## $ new_deaths <dbl> 0, 9, 0, 0, 0, 1, 7, 11, 2, 8, 5, 6, 0, 100, 0, 5, 0, 0…
## $ pnew_death <dbl> 0, 0, NA, NA, NA, 0, 2, 0, 0, 3, 1, 0, 0, 34, 0, 0, 0, …
## $ created_at <chr> "03/12/2021 03:20:13 PM", "12/02/2021 02:35:20 PM", "04…
## $ consent_cases <chr> "Agree", "Agree", NA, "Not agree", "Not agree", "Agree"…
## $ consent_deaths <chr> "N/A", "Not agree", NA, "Not agree", "Not agree", "Not …## Rows: 50717 Columns: 135
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (132): critical_staffing_shortage_today_yes, critical_staffing_shortage...
## lgl (1): geocoded_state
## date (1): date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 25
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2022-09-06 inp 35899 31213 4686 0.1396472
## 2 2020-07-25 hosp_ped 4543 3964 579 0.1361232
## 3 2022-09-06 hosp_ped 1651 1543 108 0.0676268
## 4 2022-09-06 hosp_adult 34457 29670 4787 0.1492975
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 KY inp 816280 813897 2383 0.002923609
## 2 NC inp 1500803 1499030 1773 0.001182066
## 3 ME hosp_ped 2525 2573 48 0.018830914
## 4 SC hosp_ped 9677 9528 149 0.015516793
## 5 WV hosp_ped 6031 6124 93 0.015302345
## 6 DE hosp_ped 5656 5585 71 0.012632328
## 7 KY hosp_ped 21854 21680 174 0.007993752
## 8 MS hosp_ped 12564 12470 94 0.007509787
## 9 NJ hosp_ped 20426 20300 126 0.006187693
## 10 MA hosp_ped 13350 13270 80 0.006010518
## 11 MD hosp_ped 18441 18334 107 0.005819171
## 12 UT hosp_ped 10918 10980 62 0.005662618
## 13 VA hosp_ped 19166 19260 94 0.004892521
## 14 NM hosp_ped 8390 8351 39 0.004659220
## 15 ID hosp_ped 4294 4275 19 0.004434590
## 16 CO hosp_ped 22949 23039 90 0.003914065
## 17 KS hosp_ped 5109 5128 19 0.003712025
## 18 NV hosp_ped 5680 5661 19 0.003350675
## 19 AR hosp_ped 13462 13506 44 0.003263127
## 20 TN hosp_ped 23438 23376 62 0.002648780
## 21 PA hosp_ped 57278 57151 127 0.002219717
## 22 RI hosp_ped 3719 3727 8 0.002148805
## 23 MT hosp_ped 3511 3518 7 0.001991748
## 24 IL hosp_ped 46107 46022 85 0.001845239
## 25 PR hosp_ped 24430 24470 40 0.001635992
## 26 MO hosp_ped 41624 41556 68 0.001635008
## 27 SD hosp_ped 4436 4443 7 0.001576754
## 28 NC hosp_ped 31811 31761 50 0.001573020
## 29 AL hosp_ped 21961 21929 32 0.001458191
## 30 GA hosp_ped 55159 55238 79 0.001431198
## 31 AK hosp_ped 2922 2926 4 0.001367989
## 32 KY hosp_adult 743511 741160 2351 0.003167032
## 33 NC hosp_adult 1376148 1374437 1711 0.001244099
## 34 VT hosp_adult 25440 25466 26 0.001021491
##
##
##
## Raw file for cdcHosp:
## Rows: 50,717
## Columns: 135
## $ state <chr> …
## $ date <date> …
## $ critical_staffing_shortage_today_yes <dbl> …
## $ critical_staffing_shortage_today_no <dbl> …
## $ critical_staffing_shortage_today_not_reported <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> …
## $ hospital_onset_covid <dbl> …
## $ hospital_onset_covid_coverage <dbl> …
## $ inpatient_beds <dbl> …
## $ inpatient_beds_coverage <dbl> …
## $ inpatient_beds_used <dbl> …
## $ inpatient_beds_used_coverage <dbl> …
## $ inp <dbl> …
## $ inpatient_beds_used_covid_coverage <dbl> …
## $ previous_day_admission_adult_covid_confirmed <dbl> …
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> …
## $ previous_day_admission_adult_covid_suspected <dbl> …
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_suspected <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> …
## $ staffed_adult_icu_bed_occupancy <dbl> …
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> …
## $ hosp_adult <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> …
## $ hosp_ped <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> …
## $ total_staffed_adult_icu_beds <dbl> …
## $ total_staffed_adult_icu_beds_coverage <dbl> …
## $ inpatient_beds_utilization <dbl> …
## $ inpatient_beds_utilization_coverage <dbl> …
## $ inpatient_beds_utilization_numerator <dbl> …
## $ inpatient_beds_utilization_denominator <dbl> …
## $ percent_of_inpatients_with_covid <dbl> …
## $ percent_of_inpatients_with_covid_coverage <dbl> …
## $ percent_of_inpatients_with_covid_numerator <dbl> …
## $ percent_of_inpatients_with_covid_denominator <dbl> …
## $ inpatient_bed_covid_utilization <dbl> …
## $ inpatient_bed_covid_utilization_coverage <dbl> …
## $ inpatient_bed_covid_utilization_numerator <dbl> …
## $ inpatient_bed_covid_utilization_denominator <dbl> …
## $ adult_icu_bed_covid_utilization <dbl> …
## $ adult_icu_bed_covid_utilization_coverage <dbl> …
## $ adult_icu_bed_covid_utilization_numerator <dbl> …
## $ adult_icu_bed_covid_utilization_denominator <dbl> …
## $ adult_icu_bed_utilization <dbl> …
## $ adult_icu_bed_utilization_coverage <dbl> …
## $ adult_icu_bed_utilization_numerator <dbl> …
## $ adult_icu_bed_utilization_denominator <dbl> …
## $ geocoded_state <lgl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> …
## $ deaths_covid <dbl> …
## $ deaths_covid_coverage <dbl> …
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses <dbl> …
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses <dbl> …
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses <dbl> …
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used <dbl> …
## $ previous_week_therapeutic_b_bamlanivimab_courses_used <dbl> …
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used <dbl> …
## $ icu_patients_confirmed_influenza <dbl> …
## $ icu_patients_confirmed_influenza_coverage <dbl> …
## $ previous_day_admission_influenza_confirmed <dbl> …
## $ previous_day_admission_influenza_confirmed_coverage <dbl> …
## $ previous_day_deaths_covid_and_influenza <dbl> …
## $ previous_day_deaths_covid_and_influenza_coverage <dbl> …
## $ previous_day_deaths_influenza <dbl> …
## $ previous_day_deaths_influenza_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_coverage <dbl> …
## $ all_pediatric_inpatient_bed_occupied <dbl> …
## $ all_pediatric_inpatient_bed_occupied_coverage <dbl> …
## $ all_pediatric_inpatient_beds <dbl> …
## $ all_pediatric_inpatient_beds_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11 <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_coverage <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_coverage <dbl> …
## $ staffed_pediatric_icu_bed_occupancy <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_coverage <dbl> …
## $ total_staffed_pediatric_icu_beds <dbl> …
## $ total_staffed_pediatric_icu_beds_coverage <dbl> …## Warning: One or more parsing issues, see `problems()` for details## Rows: 36440 Columns: 101
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Date, Location
## dbl (93): MMWR_week, Distributed, Distributed_Janssen, Distributed_Moderna, ...
## lgl (6): Second_Booster, Administered_Bivalent, Admin_Bivalent_PFR, Admin_B...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference: Administered_Bivalent Admin_Bivalent_PFR Admin_Bivalent_MOD Dist_Bivalent_PFR Dist_Bivalent_MOD
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 4
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 36,440
## Columns: 101
## $ date <date> 2022-09-28, 2022-09-28, 2022-0…
## $ MMWR_week <dbl> 39, 39, 39, 39, 39, 39, 39, 39,…
## $ state <chr> "VA2", "NE", "DE", "WV", "IA", …
## $ Distributed <dbl> 8845320, 4747440, 2836355, 4821…
## $ Distributed_Janssen <dbl> 626900, 151900, 101200, 170200,…
## $ Distributed_Moderna <dbl> 4313180, 1627380, 1078600, 1924…
## $ Distributed_Pfizer <dbl> 3898140, 2963760, 1653155, 2719…
## $ Distributed_Novavax <dbl> 7100, 4400, 3400, 7000, 12500, …
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Dist_Per_100K <dbl> 0, 245421, 291277, 269043, 2530…
## $ Distributed_Per_100k_5Plus <dbl> 0, 263231, 308620, 283773, 2698…
## $ Distributed_Per_100k_12Plus <dbl> 0, 293521, 337739, 309509, 2983…
## $ Distributed_Per_100k_18Plus <dbl> 0, 325539, 368266, 336571, 3288…
## $ Distributed_Per_100k_65Plus <dbl> 0, 1519390, 1501460, 1313760, 1…
## $ vxa <dbl> 7846098, 3451226, 1970701, 2901…
## $ Administered_5Plus <dbl> 7845919, 3437511, 1963888, 2896…
## $ Administered_12Plus <dbl> 7845897, 3306108, 1902046, 2841…
## $ Administered_18Plus <dbl> 7842493, 3086344, 1785352, 2709…
## $ Administered_65Plus <dbl> 4223864, 970237, 620955, 983314…
## $ Administered_Janssen <dbl> 256285, 95847, 62987, 68294, 18…
## $ Administered_Moderna <dbl> 4052239, 1227878, 756774, 12486…
## $ Administered_Pfizer <dbl> 3537422, 2118721, 1148333, 1582…
## $ Administered_Novavax <dbl> 111, 194, 88, 95, 271, 193, 206…
## $ Administered_Unk_Manuf <dbl> 41, 8586, 2519, 2161, 1283, 462…
## $ Admin_Per_100k <dbl> 0, 178413, 202380, 161918, 1748…
## $ Admin_Per_100k_5Plus <dbl> 0, 190599, 213688, 170493, 1856…
## $ Admin_Per_100k_12Plus <dbl> 0, 204408, 226486, 182420, 1987…
## $ Admin_Per_100k_18Plus <dbl> 0, 211635, 231806, 189157, 2064…
## $ Admin_Per_100k_65Plus <dbl> 0, 310518, 328711, 267925, 3140…
## $ Recip_Administered <dbl> 7846098, 3471776, 1946433, 2908…
## $ Administered_Dose1_Recip <dbl> 3545905, 1393418, 835939, 11911…
## $ Administered_Dose1_Pop_Pct <dbl> 0.0, 72.0, 85.8, 66.5, 69.5, 62…
## $ Administered_Dose1_Recip_5Plus <dbl> 3545797, 1385254, 832301, 11881…
## $ Administered_Dose1_Recip_5PlusPop_Pct <dbl> 0.0, 76.8, 90.6, 69.9, 73.6, 66…
## $ Administered_Dose1_Recip_12Plus <dbl> 3545781, 1321056, 800850, 11585…
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 0.0, 81.7, 95.0, 74.4, 78.2, 72…
## $ Administered_Dose1_Recip_18Plus <dbl> 3543854, 1221971, 749018, 10956…
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 0.0, 83.8, 95.0, 76.5, 80.5, 75…
## $ Administered_Dose1_Recip_65Plus <dbl> 1729338, 313542, 218259, 347461…
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 0.0, 95.0, 95.0, 94.7, 95.0, 95…
## $ vxc <dbl> 2988861, 1257663, 694914, 10549…
## $ vxcpoppct <dbl> 0.0, 65.0, 71.4, 58.9, 63.2, 55…
## $ Series_Complete_5Plus <dbl> 2988790, 1254345, 693868, 10537…
## $ Series_Complete_5PlusPop_Pct <dbl> 0.0, 69.5, 75.5, 62.0, 67.3, 59…
## $ Series_Complete_12Plus <dbl> 2988784, 1198619, 668526, 10302…
## $ Series_Complete_12PlusPop_Pct <dbl> 0.0, 74.1, 79.6, 66.1, 71.6, 63…
## $ vxcgte18 <dbl> 2987373, 1108987, 624194, 97492…
## $ vxcgte18pct <dbl> 0.0, 76.0, 81.0, 68.1, 73.7, 66…
## $ vxcgte65 <dbl> 1528522, 292897, 187725, 316675…
## $ vxcgte65pct <dbl> 0.0, 93.7, 95.0, 86.3, 94.7, 88…
## $ Series_Complete_Janssen <dbl> 233980, 89549, 57869, 61920, 16…
## $ Series_Complete_Moderna <dbl> 1467192, 424332, 242886, 432285…
## $ Series_Complete_Pfizer <dbl> 1287661, 741342, 392832, 559945…
## $ Series_Complete_Novavax <dbl> 24, 59, 29, 30, 50, 91, 651, 28…
## $ Series_Complete_Unk_Manuf <dbl> 3, 2100, 861, 589, 643, 1010, 4…
## $ Series_Complete_Janssen_5Plus <dbl> 233974, 89532, 57865, 61903, 16…
## $ Series_Complete_Moderna_5Plus <dbl> 1467169, 421449, 242494, 431420…
## $ Series_Complete_Pfizer_5Plus <dbl> 1287620, 741211, 392619, 559781…
## $ Series_Complete_Unk_Manuf_5Plus <dbl> 3, 2094, 861, 587, 642, 1010, 4…
## $ Series_Complete_Janssen_12Plus <dbl> 233973, 89519, 57858, 61901, 16…
## $ Series_Complete_Moderna_12Plus <dbl> 1467168, 421331, 242451, 431343…
## $ Series_Complete_Pfizer_12Plus <dbl> 1287616, 685669, 367340, 536395…
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 3, 2041, 848, 572, 606, 1004, 4…
## $ Series_Complete_Janssen_18Plus <dbl> 233938, 89441, 57808, 61838, 16…
## $ Series_Complete_Moderna_18Plus <dbl> 1467129, 421072, 242277, 430941…
## $ Series_Complete_Pfizer_18Plus <dbl> 1286279, 596543, 323272, 481581…
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 3, 1877, 808, 533, 554, 996, 43…
## $ Series_Complete_Janssen_65Plus <dbl> 76712, 7058, 10082, 9689, 14679…
## $ Series_Complete_Moderna_65Plus <dbl> 835134, 141612, 77853, 162729, …
## $ Series_Complete_Pfizer_65Plus <dbl> 616669, 143226, 99394, 144029, …
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 2, 993, 393, 223, 163, 373, 227…
## $ Additional_Doses <dbl> 1221492, 673360, 339672, 506667…
## $ Additional_Doses_Vax_Pct <dbl> 40.9, 53.5, 48.9, 48.0, 55.3, 4…
## $ Additional_Doses_5Plus <dbl> 1221487, 673298, 339670, 506630…
## $ Additional_Doses_5Plus_Vax_Pct <dbl> 40.9, 53.7, 49.0, 48.1, 55.4, 4…
## $ Additional_Doses_12Plus <dbl> 1221486, 662455, 335972, 504088…
## $ Additional_Doses_12Plus_Vax_Pct <dbl> 40.9, 55.3, 50.3, 48.9, 56.9, 4…
## $ Additional_Doses_18Plus <dbl> 1221397, 631848, 322036, 491562…
## $ Additional_Doses_18Plus_Vax_Pct <dbl> 40.9, 57.0, 51.6, 50.4, 58.8, 4…
## $ Additional_Doses_50Plus <dbl> 1106281, 400915, 227926, 357348…
## $ Additional_Doses_50Plus_Vax_Pct <dbl> 46.9, 70.2, 63.2, 61.0, 71.9, 6…
## $ Additional_Doses_65Plus <dbl> 795759, 230950, 135191, 218704,…
## $ Additional_Doses_65Plus_Vax_Pct <dbl> 52.1, 78.9, 72.0, 69.1, 80.6, 7…
## $ Additional_Doses_Moderna <dbl> 646719, 255267, 142652, 234519,…
## $ Additional_Doses_Pfizer <dbl> 553511, 410083, 191595, 266805,…
## $ Additional_Doses_Janssen <dbl> 21262, 7100, 5321, 5212, 14337,…
## $ Additional_Doses_Unk_Manuf <dbl> 0, 887, 103, 129, 181, 180, 106…
## $ Second_Booster <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Second_Booster_50Plus <dbl> 289139, 153105, 88562, 115683, …
## $ Second_Booster_50Plus_Vax_Pct <dbl> 26.1, 38.2, 38.9, 32.4, 40.6, 3…
## $ Second_Booster_65Plus <dbl> 226537, 105198, 63069, 82695, 2…
## $ Second_Booster_65Plus_Vax_Pct <dbl> 28.5, 45.6, 46.7, 37.8, 49.3, 4…
## $ Second_Booster_Janssen <dbl> 58, 114, 88, 72, 100, 94, 2151,…
## $ Second_Booster_Moderna <dbl> 152100, 62272, 43047, 57960, 14…
## $ Second_Booster_Pfizer <dbl> 141364, 105351, 52049, 67207, 1…
## $ Second_Booster_Unk_Manuf <dbl> 1, 342, 27, 35, 139, 45, 4088, …
## $ Administered_Bivalent <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Admin_Bivalent_PFR <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Admin_Bivalent_MOD <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Dist_Bivalent_PFR <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Dist_Bivalent_MOD <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 3.77e+10 5.43e+8 95527960 1036655 57997
## 2 after 3.75e+10 5.40e+8 94432864 1030862 50133
## 3 pctchg 7.08e- 3 4.61e-3 0.0115 0.00559 0.136
##
##
## Processed for cdcDaily:
## Rows: 50,133
## Columns: 6
## $ date <date> 2021-03-11, 2021-12-01, 2020-04-08, 2020-02-04, 2021-09-01…
## $ state <chr> "KS", "ND", "AR", "AR", "ND", "IN", "AR", "AL", "NM", "UT",…
## $ tot_cases <dbl> 297229, 163565, 1071, 0, 118491, 668765, 56199, 547966, 602…
## $ tot_deaths <dbl> 4851, 1907, 18, 0, 1562, 12710, 674, 11530, 8318, 3787, 107…
## $ new_cases <dbl> 0, 589, 78, 0, 536, 487, 547, 154, 1509, 0, 2135, 8, 451, 0…
## $ new_deaths <dbl> 0, 9, 0, 0, 1, 7, 11, 5, 6, 0, 100, 0, 5, 0, 0, 7, 8, 39, 1…
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 5.19e+7 4.52e+7 1269286 50717
## 2 after 5.16e+7 4.50e+7 1244077 48456
## 3 pctchg 5.41e-3 5.18e-3 0.0199 0.0446
##
##
## Processed for cdcHosp:
## Rows: 48,456
## Columns: 5
## $ date <date> 2021-01-13, 2021-01-13, 2021-01-13, 2021-01-10, 2021-01-09…
## $ state <chr> "DC", "NH", "NV", "RI", "MA", "SD", "RI", "MN", "RI", "SD",…
## $ inp <dbl> 371, 294, 1817, 449, 2075, 247, 483, 1040, 471, 291, 669, 5…
## $ hosp_adult <dbl> 343, 291, 1810, 446, 2051, 244, 479, 1024, 469, 287, 665, 5…
## $ hosp_ped <dbl> 28, 3, 7, 3, 24, 3, 4, 16, 2, 4, 4, 108, 2, 1, 0, 7, 0, 1, …
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgt…¹ vxcgte18 vxcgt…² n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 4.19e+11 1.72e+11 1527989. 4.35e+10 2.26e+6 1.59e+11 1.80e+6 3.64e+4
## 2 after 2.02e+11 8.31e+10 1278920. 2.11e+10 2.00e+6 7.69e+10 1.52e+6 2.88e+4
## 3 pctchg 5.18e- 1 5.16e- 1 0.163 5.16e- 1 1.14e-1 5.16e- 1 1.54e-1 2.09e-1
## # … with abbreviated variable names ¹​vxcgte65pct, ²​vxcgte18pct
##
##
## Processed for vax:
## Rows: 28,815
## Columns: 9
## $ date <date> 2022-09-28, 2022-09-28, 2022-09-28, 2022-09-28, 2022-09-2…
## $ state <chr> "NE", "DE", "WV", "IA", "ID", "FL", "ND", "AR", "KY", "MI"…
## $ vxa <dbl> 3451226, 1970701, 2901813, 5515503, 2630561, 39803100, 117…
## $ vxc <dbl> 1257663, 694914, 1054914, 1994024, 989510, 14697269, 43395…
## $ vxcpoppct <dbl> 65.0, 71.4, 58.9, 63.2, 55.4, 68.4, 56.9, 55.8, 58.6, 61.4…
## $ vxcgte65 <dbl> 292897, 187725, 316675, 523635, 257641, 4173533, 105759, 4…
## $ vxcgte65pct <dbl> 93.7, 95.0, 86.3, 94.7, 88.6, 92.8, 88.2, 82.3, 87.9, 89.9…
## $ vxcgte18 <dbl> 1108987, 624194, 974923, 1790031, 891754, 13500109, 393211…
## $ vxcgte18pct <dbl> 76.0, 81.0, 68.1, 73.7, 66.6, 78.3, 67.6, 65.5, 68.7, 70.5…
##
## Integrated per capita data file:
## Rows: 50,346
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0…
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"…
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, …
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA…
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in Proj4 definition
saveToRDS(cdc_daily_221002, ovrWriteError=FALSE)The function is run to download and process the latest hospitalization data:
# Run for latest data, save as RDS
indivHosp_20221003 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20221003.csv")##
## File ./RInputFiles/Coronavirus/HHS_Hospital_20221003.csv already exists
## File will not be downloaded since ovrWrite is not TRUE## Rows: 260543 Columns: 128
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (11): hospital_pk, state, ccn, hospital_name, address, city, zip, hosp...
## dbl (114): total_beds_7_day_avg, all_adult_hospital_beds_7_day_avg, all_adu...
## lgl (2): is_metro_micro, is_corrected
## date (1): collection_week
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.## Rows: 260,543
## Columns: 128
## $ hospital_pk <chr> …
## $ collection_week <date> …
## $ state <chr> …
## $ ccn <chr> …
## $ hospital_name <chr> …
## $ address <chr> …
## $ city <chr> …
## $ zip <chr> …
## $ hospital_subtype <chr> …
## $ fips_code <chr> …
## $ is_metro_micro <lgl> …
## $ total_beds_7_day_avg <dbl> …
## $ all_adult_hospital_beds_7_day_avg <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_avg <dbl> …
## $ inpatient_beds_used_7_day_avg <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg <dbl> …
## $ inpatient_beds_used_covid_7_day_avg <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg <dbl> …
## $ inpatient_beds_7_day_avg <dbl> …
## $ total_icu_beds_7_day_avg <dbl> …
## $ total_staffed_adult_icu_beds_7_day_avg <dbl> …
## $ icu_beds_used_7_day_avg <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_avg <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg <dbl> …
## $ icu_patients_confirmed_influenza_7_day_avg <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg <dbl> …
## $ total_beds_7_day_sum <dbl> …
## $ all_adult_hospital_beds_7_day_sum <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_sum <dbl> …
## $ inpatient_beds_used_7_day_sum <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum <dbl> …
## $ inpatient_beds_used_covid_7_day_sum <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum <dbl> …
## $ inpatient_beds_7_day_sum <dbl> …
## $ total_icu_beds_7_day_sum <dbl> …
## $ total_staffed_adult_icu_beds_7_day_sum <dbl> …
## $ icu_beds_used_7_day_sum <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_sum <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum <dbl> …
## $ icu_patients_confirmed_influenza_7_day_sum <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum <dbl> …
## $ total_beds_7_day_coverage <dbl> …
## $ all_adult_hospital_beds_7_day_coverage <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_coverage <dbl> …
## $ inpatient_beds_used_7_day_coverage <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage <dbl> …
## $ inpatient_beds_used_covid_7_day_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> …
## $ inpatient_beds_7_day_coverage <dbl> …
## $ total_icu_beds_7_day_coverage <dbl> …
## $ total_staffed_adult_icu_beds_7_day_coverage <dbl> …
## $ icu_beds_used_7_day_coverage <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage <dbl> …
## $ icu_patients_confirmed_influenza_7_day_coverage <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage <dbl> …
## $ previous_day_admission_adult_covid_confirmed_7_day_sum <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum` <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum <dbl> …
## $ previous_day_covid_ED_visits_7_day_sum <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_sum <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum` <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum` <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum <dbl> …
## $ previous_day_total_ED_visits_7_day_sum <dbl> …
## $ previous_day_admission_influenza_confirmed_7_day_sum <dbl> …
## $ geocoded_hospital_address <chr> …
## $ hhs_ids <chr> …
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_coverage <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage <dbl> …
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_none_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_one_7_day <dbl> …
## $ total_personnel_covid_vaccinated_doses_all_7_day <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_one_7_day <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_all_7_day <dbl> …
## $ is_corrected <lgl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_avg <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_coverage <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_sum <dbl> …
## $ all_pediatric_inpatient_beds_7_day_avg <dbl> …
## $ all_pediatric_inpatient_beds_7_day_coverage <dbl> …
## $ all_pediatric_inpatient_beds_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_7_day_sum <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_7_day_sum <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_avg <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_coverage <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_sum <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_avg <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_coverage <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_sum <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_avg <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_coverage <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_sum <dbl> …
##
## Hospital Subtype Counts:
## # A tibble: 4 × 2
## hospital_subtype n
## <chr> <int>
## 1 Childrens Hospitals 4909
## 2 Critical Access Hospitals 69937
## 3 Long Term 17869
## 4 Short Term 167828
##
## Records other than 50 states and DC
## # A tibble: 5 × 2
## state n
## <chr> <int>
## 1 AS 52
## 2 GU 102
## 3 MP 40
## 4 PR 2766
## 5 VI 104
##
## Record types for key metrics
## # A tibble: 10 × 5
## name `NA` Posit…¹ Value…² Total
## <chr> <int> <int> <int> <int>
## 1 all_adult_hospital_beds_7_day_avg 71194 188847 502 260543
## 2 all_adult_hospital_inpatient_bed_occupied_7_day… 131 238657 21755 260543
## 3 icu_beds_used_7_day_avg 56 228646 31841 260543
## 4 inpatient_beds_7_day_avg 46 259479 1018 260543
## 5 inpatient_beds_used_7_day_avg 35 239484 21024 260543
## 6 inpatient_beds_used_covid_7_day_avg 26 173367 87150 260543
## 7 staffed_icu_adult_patients_confirmed_and_suspec… 169 174858 85516 260543
## 8 total_adult_patients_hospitalized_confirmed_and… 127 173350 87066 260543
## 9 total_beds_7_day_avg 69419 190826 298 260543
## 10 total_icu_beds_7_day_avg 58 246878 13607 260543
## # … with abbreviated variable names ¹​Positive, ²​`Value -999999`## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
saveToRDS(indivHosp_20221003, ovrWriteError=FALSE)Post-processing is run, including hospital summaries:
# Create pivoted burden data
burdenPivotList_221002 <- postProcessCDCDaily(cdc_daily_221002,
dataThruLabel="Sep 2022",
keyDatesBurden=c("2022-09-30", "2022-03-31",
"2021-09-30", "2021-03-31"
),
keyDatesVaccine=c("2022-09-28", "2022-03-31",
"2021-09-30", "2021-03-31"
),
returnData=TRUE
)## Joining, by = "state"##
## *** File has been checked for uniqueness by: state date name## Warning: Removed 24 row(s) containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 9 row(s) containing missing values (geom_path).
# Create hospitalized per capita data
hospPerCap_221002 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"),
lst=burdenPivotList_221002,
popVar="pop2019",
excludeState=c(),
cumStartDate="2020-07-15"
)## Warning: Removed 18 row(s) containing missing values (geom_path).
burdenPivotList_221002$hospAge %>%
group_by(adultPed, confSusp, age, name) %>%
summarize(value=sum(value, na.rm=TRUE), n=n(), .groups="drop")## # A tibble: 18 × 6
## adultPed confSusp age name value n
## <chr> <chr> <chr> <chr> <dbl> <int>
## 1 adult confirmed 0-19 previous_day_admission_adult_covid_con… 4.77e4 50717
## 2 adult confirmed 20-29 previous_day_admission_adult_covid_con… 2.90e5 50717
## 3 adult confirmed 30-39 previous_day_admission_adult_covid_con… 4.19e5 50717
## 4 adult confirmed 40-49 previous_day_admission_adult_covid_con… 5.02e5 50717
## 5 adult confirmed 50-59 previous_day_admission_adult_covid_con… 8.01e5 50717
## 6 adult confirmed 60-69 previous_day_admission_adult_covid_con… 1.05e6 50717
## 7 adult confirmed 70-79 previous_day_admission_adult_covid_con… 1.06e6 50717
## 8 adult confirmed 80+ previous_day_admission_adult_covid_con… 9.58e5 50717
## 9 adult suspected 0-19 previous_day_admission_adult_covid_sus… 3.91e4 50717
## 10 adult suspected 20-29 previous_day_admission_adult_covid_sus… 2.61e5 50717
## 11 adult suspected 30-39 previous_day_admission_adult_covid_sus… 3.43e5 50717
## 12 adult suspected 40-49 previous_day_admission_adult_covid_sus… 3.47e5 50717
## 13 adult suspected 50-59 previous_day_admission_adult_covid_sus… 5.48e5 50717
## 14 adult suspected 60-69 previous_day_admission_adult_covid_sus… 7.55e5 50717
## 15 adult suspected 70-79 previous_day_admission_adult_covid_sus… 7.36e5 50717
## 16 adult suspected 80+ previous_day_admission_adult_covid_sus… 6.70e5 50717
## 17 ped confirmed 0-19 previous_day_admission_pediatric_covid… 1.73e5 50717
## 18 ped suspected 0-19 previous_day_admission_pediatric_covid… 3.88e5 50717saveToRDS(burdenPivotList_221002, ovrWriteError=FALSE)
saveToRDS(hospPerCap_221002, ovrWriteError=FALSE)Peaks and valleys of key metrics are also updated:
peakValleyCDCDaily(cdc_daily_221002)## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## # A tibble: 8,040 × 8
## date state vxa vxc vxa_isPeak vxc_isPeak vxa_isValley vxc_isValley
## <date> <chr> <dbl> <dbl> <lgl> <lgl> <lgl> <lgl>
## 1 2020-12-01 CA NA NA FALSE FALSE FALSE FALSE
## 2 2020-12-01 FL NA NA FALSE FALSE FALSE FALSE
## 3 2020-12-01 GA NA NA FALSE FALSE FALSE FALSE
## 4 2020-12-01 IL NA NA FALSE FALSE FALSE FALSE
## 5 2020-12-01 MI NA NA FALSE FALSE FALSE FALSE
## 6 2020-12-01 NC NA NA FALSE FALSE FALSE FALSE
## 7 2020-12-01 NJ NA NA FALSE FALSE FALSE FALSE
## 8 2020-12-01 NY NA NA FALSE FALSE FALSE FALSE
## 9 2020-12-01 OH NA NA FALSE FALSE FALSE FALSE
## 10 2020-12-01 PA NA NA FALSE FALSE FALSE FALSE
## # … with 8,030 more rows
## # ℹ Use `print(n = ...)` to see more rowsA function is written for the hospital data availability
checks:
Data availability by source and time is assessed:
checkHospitalDataComplete <- function(df1=NULL,
df2=NULL,
dfAll=NULL,
lab1="DF1",
lab2="DF2",
trendBreaks=c(),
makeP1=TRUE,
makeP2=TRUE,
returnData=FALSE
) {
# FUNCTION ARGUMENTS:
# df1: the first data frame (NULL means integrated frame passed as dfAll)
# df2: the second data frame (NULL means integrated frame passed as dfAll)
# dfAll: integrated data frame from previous iteration of function (will ignore df1 and df2)
# lab1: plot label for the first data frame
# lab2: plot label for the second data frame
# trendBreaks: character vector of trend break dates of form YYYY-MM-DD - if c(), no trend brek vlines plotted
# makeP1: boolean, should the first plot be created and printed?
# makeP2: boolean, should the first plot be created and printed?
# returnData: boolean, should dfAll be returned?
# Temporary function to aggregate data
tempCountComplete <- function(df) {
df %>%
select(hospital_pk, collection_week, all_of(names(hhsMapper))) %>%
colRenamer(vecRename=hhsMapper) %>%
pivot_longer(-c(hospital_pk, collection_week)) %>%
filter(!is.na(value), value>0) %>%
count(collection_week, name)
}
# Create or use passed data
if(is.null(dfAll)) {
if(is.null(df1) | is.null(df2)) stop("dfAll not passed requires both df1 and df2 to be passed\n")
dfAll <- bind_rows(tempCountComplete(df1), tempCountComplete(df2), .id="src")
} else {
if(!is.null(df1) | !is.null(df2)) warning("dfAll passed and will be used; df1 and/or df2 ignored\n")
}
# Plot data completeness - hospitals reporting >0 on metric by week
if(isTRUE(makeP1)) {
p1 <- dfAll %>%
select(collection_week, name) %>%
unique() %>%
bind_rows(., ., .id="src") %>%
full_join(dfAll, by=c("src", "collection_week", "name")) %>%
mutate(src=c("1"=lab1, "2"=lab2)[src]) %>%
mutate(n=ifelse(is.na(n), 0, n)) %>%
ggplot(aes(x=collection_week, y=n)) +
geom_line(aes(group=src, color=src)) +
facet_wrap(~name) +
labs(title="Number of hospitals in US reporting >0 on metric by week",
x=NULL,
y="# Hospitals Reporting > 0"
) +
scale_color_discrete("Data Source:")
if(length(trendBreaks) > 0) {
p1 <- p1 + geom_vline(xintercept=as.Date(all_of(trendBreaks)), lty=2) +
labs(subtitle=paste0("Trend break dashed lines at ",
paste0(all_of(trendBreaks), collapse=" and ")
)
)
}
print(p1)
}
# Plot data completeness - showing difference in reported data and trend break dates
if(isTRUE(makeP2)) {
p2 <- dfAll %>%
select(collection_week, name) %>%
unique() %>%
bind_rows(., ., .id="src") %>%
full_join(dfAll, by=c("src", "collection_week", "name")) %>%
mutate(src=c("1"=lab1, "2"=lab2)[src]) %>%
mutate(n=ifelse(is.na(n), 0, n)) %>%
group_by(collection_week, name) %>%
summarize(delta=sum(ifelse(src==lab2, n, 0)-ifelse(src!=lab2, n, 0)), .groups="drop") %>%
ggplot(aes(x=collection_week, y=delta)) +
geom_line(aes(color=case_when(delta>=0 ~ "darkgreen", TRUE ~ "red"))) +
geom_hline(yintercept=0, lty=2) +
scale_color_identity(NULL) +
facet_wrap(~name) +
labs(title="Delta in Number of hospitals in US reporting >0 on metric by week",
x=NULL,
y="Delta in # Hospitals Reporting > 0"
)
if(length(trendBreaks) > 0) {
p2 <- p2 + geom_vline(xintercept=as.Date(all_of(trendBreaks)), lty=2) +
labs(subtitle=paste0("Trend break dashed lines at ",
paste0(all_of(trendBreaks), collapse=" and ")
)
)
}
print(p2)
}
if(isTRUE(returnData)) return(dfAll)
}
# Create the data
dfTemp <- checkHospitalDataComplete(df1=readFromRDS("indivHosp_20221003"),
df2=readFromRDS("indivHosp_20220704"),
makeP1=FALSE,
makeP2=FALSE,
returnData=TRUE
)
# Create the first plot
checkHospitalDataComplete(dfAll=dfTemp,
lab1="OCT-2022",
lab2="JUL-2022",
makeP2=FALSE,
trendBreaks=c("2021-09-25", "2022-06-24")
)
# Create the second plot
checkHospitalDataComplete(dfAll=dfTemp,
lab1="OCT-2022",
lab2="JUL-2022",
makeP1=FALSE,
trendBreaks=c("2021-10-30", "2022-06-24")
)
Plot 2 is not working as intended (discontinuity of the difference line) and should be updated
Hospital data are pieced together as needed:
# Create modified hospital data
# multiSourceHosp_20221002 <- multiSourceDataCombine(list(readFromRDS("indivHosp_20220704"),
# indivHosp_20221003
# ),
# timeVec=as.Date("2022-01-01")
# )
# Confirm that function produces expected output
# multiSourceHosp_20221002 %>%
# select(-src) %>%
# identical(modHospData %>% select(-src))